The OWASP Top 10 for LLM Applications is a great starting point for understanding AI security.
But knowing the risks isn't enough.
The real question is
๐ช๐ต๐ฎ๐ ๐ฑ๐ผ ๐๐ผ๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ถ๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐ ๐๐ผ ๐บ๐ถ๐๐ถ๐ด๐ฎ๐๐ฒ ๐๐ต๐ฒ๐บ?
When analyzing the OWASP LLM risks from an engineering perspective, I see a recurring pattern: many vulnerabilities cannot be solved by simply adding another prompt instruction or filtering a few keywords.
They require ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐ ๐ฐ๐ผ๐ป๐๐ฟ๐ผ๐น๐ ๐ฎ๐ ๐๐ต๐ฒ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ ๐น๐ฒ๐๐ฒ๐น.
Here is how I approach the major risk categories:
๐น ๐๐๐ ๐ฌ๐ญ โ ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ป๐ท๐ฒ๐ฐ๐๐ถ๐ผ๐ป
โ Implement prompt gateways, input isolation, content validation, trust boundaries and adversarial testing.
๐น ๐๐๐ ๐ฌ๐ฎ โ ๐ฆ๐ฒ๐ป๐๐ถ๐๐ถ๐๐ฒ ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐๐ถ๐๐ฐ๐น๐ผ๐๐๐ฟ๐ฒ
โ Apply data classification, least-privilege access, output filtering and strict separation of system/user data.
๐น ๐๐๐ ๐ฌ๐ฏ โ ๐ฆ๐๐ฝ๐ฝ๐น๐ ๐๐ต๐ฎ๐ถ๐ป
โ Establish model and dependency provenance, integrity verification, SBOMs, controlled model registries and continuous scanning.
๐น ๐๐๐ ๐ฌ๐ฐ โ ๐๐ฎ๐๐ฎ ๐ฎ๐ป๐ฑ ๐ ๐ผ๐ฑ๐ฒ๐น ๐ฃ๐ผ๐ถ๐๐ผ๐ป๐ถ๐ป๐ด
โ Validate training/RAG data, establish data provenance, monitor ingestion pipelines and detect anomalous content.
๐น ๐๐๐ ๐ฌ๐ฑ โ ๐๐บ๐ฝ๐ฟ๐ผ๐ฝ๐ฒ๐ฟ ๐ข๐๐๐ฝ๐๐ ๐๐ฎ๐ป๐ฑ๐น๐ถ๐ป๐ด
โ Treat LLM output as untrusted input. Validate, sanitize and constrain outputs before passing them to downstream systems.
๐น ๐๐๐ ๐ฌ๐ฒ โ ๐๐
๐ฐ๐ฒ๐๐๐ถ๐๐ฒ ๐๐ด๐ฒ๐ป๐ฐ๐
โ Use least privilege, capability restrictions, approval workflows and explicit tool authorization.
๐น ๐๐๐ ๐ฌ๐ณ โ ๐ฆ๐๐๐๐ฒ๐บ ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ฒ๐ฎ๐ธ๐ฎ๐ด๐ฒ
โ Never treat the system prompt as a security boundary. Move sensitive controls into enforceable backend policies.
๐น ๐๐๐ ๐ฌ๐ด โ ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฎ๐ป๐ฑ ๐๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด ๐ช๐ฒ๐ฎ๐ธ๐ป๐ฒ๐๐๐ฒ๐
โ Secure the entire RAG pipeline: document ingestion, chunking, metadata, embeddings, retrieval and access control.
๐น ๐๐๐ ๐ฌ๐ต โ ๐ ๐ถ๐๐ถ๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป
โ Introduce grounding, provenance, retrieval validation, confidence mechanisms and human oversight for high-impact decisions.
๐น ๐๐๐ ๐ญ๐ฌ โ ๐จ๐ป๐ฏ๐ผ๐๐ป๐ฑ๐ฒ๐ฑ ๐๐ผ๐ป๐๐๐บ๐ฝ๐๐ถ๐ผ๐ป
โ Apply rate limiting, quotas, resource controls, request validation and cost monitoring.
But there is a deeper lesson here.
๐๐ ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐ ๐ถ๐ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ถ๐ป๐ด๐น๐ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ถ๐ป๐ด ๐ฎ๐ป ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฎ๐น ๐ฑ๐ถ๐๐ฐ๐ถ๐ฝ๐น๐ถ๐ป๐ฒ.
A secure AI application needs more than a secure model.
It needs security controls around:
โ Identity & trust
โ Prompt security
โ Retrieval
โ Memory
โ Tool execution
โ Policy enforcement
โ Data flows
โ Observability
โ Runtime behaviour
This is one of the areas I am exploring through ๐ก๐ฒ๐๐ฟ๐ฎ๐น๐ฆ๐๐ฎ๐ฐ๐ธ | ๐ ๐ฆ - ๐๐ ๐ฆ๐ฒ๐ฐ๐๐ฟ๐ถ๐๐: translating AI security research and frameworks into practical architectures, engineering patterns and security controls that can actually be implemented.
My goal isn't simply to answer:
โ๐ช๐ต๐ฎ๐ ๐ฐ๐ฎ๐ป ๐ด๐ผ ๐๐ฟ๐ผ๐ป๐ด?โ
It is to answer:
โ๐๐ผ๐ ๐ฑ๐ผ ๐๐ฒ ๐ฒ๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ ๐๐ต๐ฒ ๐๐๐๐๐ฒ๐บ ๐๐ผ ๐๐ต๐ฎ๐ ๐ถ๐ ๐ถ๐ ๐ต๐ฎ๐ฟ๐ฑ๐ฒ๐ฟ ๐๐ผ ๐ด๐ผ ๐๐ฟ๐ผ๐ป๐ด ๐ถ๐ป ๐๐ต๐ฒ ๐ณ๐ถ๐ฟ๐๐ ๐ฝ๐น๐ฎ๐ฐ๐ฒ?โ
Iโll be breaking down these areas in more detail through my AI Security Engineering research and projects.
If you're building LLM applications, RAG systems or agentic AI, this is a security conversation worth having.
(Post revised using GPT)
